A review of epileptic seizure detection using machine learning classifiers
Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain
signals produced by brain neurons. Neurons are connected to each other in a complex way …
signals produced by brain neurons. Neurons are connected to each other in a complex way …
Machine learning algorithms for epilepsy detection based on published EEG databases: A systematic review
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
Epileptic seizure detection and prediction in EEGS using power spectra density parameterization
S Liu, J Wang, S Li, L Cai - IEEE Transactions on Neural …, 2023 - ieeexplore.ieee.org
Power spectrum analysis is one of the effective tools for classifying epileptic signals based
on electroencephalography (EEG) recordings. However, the conflation of periodic and …
on electroencephalography (EEG) recordings. However, the conflation of periodic and …
Six-center assessment of CNN-Transformer with belief matching loss for patient-independent seizure detection in EEG
Neurologists typically identify epileptic seizures from electroencephalograms (EEGs) by
visual inspection. This process is often time-consuming, especially for EEG recordings that …
visual inspection. This process is often time-consuming, especially for EEG recordings that …
Optimization of epilepsy detection method based on dynamic EEG channel screening
Y Song, C Fan, X Mao - Neural Networks, 2024 - Elsevier
To decrease the interference in the process of epileptic feature extraction caused by
insufficient detection capability in partial channels of focal epilepsy, this paper proposes a …
insufficient detection capability in partial channels of focal epilepsy, this paper proposes a …
An Epileptic Seizure Detection Technique Using EEG Signals with Mobile Application Development
Epileptic seizure detection classification distinguishes between epileptic and non-epileptic
signals and is an important step that can aid doctors in diagnosing and treating epileptic …
signals and is an important step that can aid doctors in diagnosing and treating epileptic …
DeepSOZ: A Robust Deep Model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data
We propose a robust deep learning framework to simultaneously detect and localize seizure
activity from multichannel scalp EEG. Our model, called DeepSOZ, consists of a transformer …
activity from multichannel scalp EEG. Our model, called DeepSOZ, consists of a transformer …
Online detection and removal of eye blink artifacts from electroencephalogram
The most prominent type of artifact contaminating electroencephalogram (EEG) signals are
the eye blink (EB) artifacts, which could potentially lead to misinterpretation of the EEG …
the eye blink (EB) artifacts, which could potentially lead to misinterpretation of the EEG …
Epileptic focus localization using transfer learning on multi-modal EEG
Y Yang, F Li, J Luo, X Qin, D Huang - Frontiers in Computational …, 2023 - frontiersin.org
The standard treatments for epilepsy are drug therapy and surgical resection. However,
around 1/3 of patients with intractable epilepsy are drug-resistant, requiring surgical …
around 1/3 of patients with intractable epilepsy are drug-resistant, requiring surgical …
[HTML][HTML] Seizure Onset Zone Detection Based on Convolutional Neural Networks and EEG Signals
Background: The localization of seizure onset zones (SOZs) is a critical step before the
surgical treatment of epilepsy. Methods and Results: In this paper, we propose an SOZ …
surgical treatment of epilepsy. Methods and Results: In this paper, we propose an SOZ …